FogGuru: a Fog Computing platform based on Apache Flink

Battulga, Davaadorj; Miorandi, Daniele; Tedeschi, Cédric

doi:10.1109/icin48450.2020.9059374

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…In-memory processing, distributed processing, ideal for analytics in real time, resilient to faults Apache Flink [29] Open source software, pipeline architecture, real-time analytics, large-scale data processing unit Iteration processing is faster, memory management is better, latency is lower, and garbage collection is more efficient than Spark.…”

Section: Software Platform Unique Features Advantagesmentioning

confidence: 99%

Prescriptive Analytical Models for Dynamic IoT Data Streams: A Review

T V,

Dilip Kumar,

K R

2024

IJCDS

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The application of data analysis tools and procedures to perceive value from vast volume of data created by connected IoT devices is known as IoT data analytics. While predictive analytics on IoT dealing with the prediction involved with the setting of IoT appliances, Prescriptive analytics is the next stage of IoT data analytics involves deriving actionable insights from predictions made in previous stages. The incorporation of time-dependent parameters in prescriptive models provides a more accurate depiction of a complex environment and the decision-making process that goes along with it. The scope of our work is to recommend prescriptive analytical models that make better decisions through the analysis of dynamic IoT data stream in real-time and prescribe an optimal solution. We carry out an analysis of time-series data to identify the patterns of data and learn how they change. In this direction, we attempt to represent time-series data by reducing its length, forecast change points, map change points to prescribed actions, and propose optimal decisions ahead of time events. In this paper, an overview of IoT data analytics, survey of prescriptive analytical models, applications, issues, challenges and platforms for IoT analytics are discussed.

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Section: Software Platform Unique Features Advantagesmentioning

confidence: 99%

Prescriptive Analytical Models for Dynamic IoT Data Streams: A Review

T V,

Dilip Kumar,

K R

2024

IJCDS

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“…Architectures Battulga, Miorandi & Tedeschi (2020) introduce the FogGuru platform for fog computing implemented via a real-world testbed. Their representative fog landscape is built out of five Raspberry Pis united in a cluster cloud tier.…”

Section: Related Workmentioning

confidence: 99%

FogFrame: a framework for IoT application execution in the fog

Skarlat¹,

Schulte²

2021

PeerJ Computer Science

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Recently, a multitude of conceptual architectures and theoretical foundations for fog computing have been proposed. Despite this, there is still a lack of concrete frameworks to setup real-world fog landscapes. In this work, we design and implement the fog computing framework FogFrame—a system able to manage and monitor edge and cloud resources in fog landscapes and to execute Internet of Things (IoT) applications. FogFrame provides communication and interaction as well as application management within a fog landscape, namely, decentralized service placement, deployment and execution. For service placement, we formalize a system model, define an objective function and constraints, and solve the problem implementing a greedy algorithm and a genetic algorithm. The framework is evaluated with regard to Quality of Service parameters of IoT applications and the utilization of fog resources using a real-world operational testbed. The evaluation shows that the service placement is adapted according to the demand and the available resources in the fog landscape. The greedy placement leads to the maximum utilization of edge devices keeping at the edge as many services as possible, while the placement based on the genetic algorithm keeps devices from overloads by balancing between the cloud and edge. When comparing edge and cloud deployment, the service deployment time at the edge takes 14% of the deployment time in the cloud. If fog resources are utilized at maximum capacity, and a new application request arrives with the need of certain sensor equipment, service deployment becomes impossible, and the application needs to be delegated to other fog resources. The genetic algorithm allows to better accommodate new applications and keep the utilization of edge devices at about 50% CPU. During the experiments, the framework successfully reacts to runtime events: (i) services are recovered when devices disappear from the fog landscape; (ii) cloud resources and highly utilized devices are released by migrating services to new devices; (iii) and in case of overloads, services are migrated in order to release resources.

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“…These software tools are not specific to fog computing solutions, but they are deployed by most public cloud providers such as Amazon AWS, Google Cloud, and Microsoft Azure to support large-scale data processing. They have also been used or suggested in many fog-based solutions [101][102][103].…”

Section: Implementation Scenariomentioning

confidence: 99%

Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review

Badidi¹,

Mahrez

Sabir

2020

Future Internet

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Demographic growth in urban areas means that modern cities face challenges in ensuring a steady supply of water and electricity, smart transport, livable space, better health services, and citizens’ safety. Advances in sensing, communication, and digital technologies promise to mitigate these challenges. Hence, many smart cities have taken a new step in moving away from internal information technology (IT) infrastructure to utility-supplied IT delivered over The Internet. The benefit of this move is to manage The vast amounts of data generated by The various city systems, including water and electricity systems, The waste management system, transportation system, public space management systems, health and education systems, and many more. Furthermore, many smart city applications are time-sensitive and need to quickly analyze data to react promptly to The various events occurring in a city. The new and emerging paradigms of edge and fog computing promise to address big data storage and analysis in The field of smart cities. Here, we review existing service delivery models in smart cities and present our perspective on adopting these two emerging paradigms. We specifically describe The design of a fog-based data pipeline to address The issues of latency and network bandwidth required by time-sensitive smart city applications.

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FogGuru: a Fog Computing platform based on Apache Flink

Cited by 7 publications

References 6 publications

Prescriptive Analytical Models for Dynamic IoT Data Streams: A Review

Prescriptive Analytical Models for Dynamic IoT Data Streams: A Review

FogFrame: a framework for IoT application execution in the fog

Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review

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